isprsarchives XL 7 W3 495 2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
SPECTRAL MIXTURE ANALYSIS (SMA) OF LANDSAT IMAGERY FOR LAND
COVER STUDY OF HIGHLY DEGRADED PEATLAND IN INDONESIA
A. D. Saktia,b*, S. Tsuyuki b
a.Center
b. Global
for Remote Sensing, Bandung Institute of Technology, Bandung, Indonesia -anjardimara@yahoo.com
Forest Environmental Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
- tsuyuki@fr.a.u-tokyo.ac.jp
Commission VI, WG VI/4
KEY WORDS: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction,
Pelalawan District.
ABSTRACT:
Indonesian peatland, one of the world’s largest tropical peatlands, is facing immense anthropogenic pressures such as illegal
logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to
assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of
land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum
Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were
compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was
used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation
(peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were
obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect
high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.
1. INTRODUCTION
Indonesia is one of the countries having the largest tropical
peatlands area in the world. The country’s peatland is facing
immense anthropogenic pressures such as illegal logging,
degradation and peat fires, especially in fertile peatlands
(Margono, 2014). However, there still is a knowledge gap in
terms of understanding extent of pressure faced by peatlands
as well as appropriate tools to assess land cover change.
Remote sensing technology is one of the useful tools for
monitoring condition of forest ecosystem such as
deforestation and forest degradation (Souza, 2005).
Land-cover classification using medium spatial resolution
data such as Landsat is often difficult because of limitations
in spatial resolution of the data and the heterogeneity of
features on the ground especially for successional stages in a
degraded tropical forest region (Souza, 2000; Stone, 1998;
Eraldo, 2010). This problem often produces poor
classification accuracy when conventional algorithms such as
the maximum likelihood classifier are used. Several methods
have been developed to solve the mixed pixels problem, one
of them being the Spectral Mixture Analysis (SMA).
The main objective of this paper is to determine number of
land cover endmembers that can be detected and mapped
using new generation of Landsat 8 OLI in order to develop
high-quality burned peat fraction images.
* Corresponding author
In order to achieve the objective, two different image
transformations, pure pixel identification method, and two
different scatterplot strategies were tested and their accuracy
results were compared.
2. STUDY AREA AND PRE-ANALYSIS OF
IMAGERY DATA
The study was conducted in the Pelalawan district, one of the
highly degraded peatland area in Indonesia (Tanuwijaya et
al., 2014; Supriatna, 2014). It is located between 100˚42' and
103˚28' E longitudes and 1˚25' and 0˚20' S latitudes, and the
district is 77 km from Pekanbaru (the capital of Riau
province) (Figure 1a). Land cover condition in Pelalawan
region consisted of land with a high peat content (wet) and
dry land (Meilani et al., 2011). Landsat 8 OLI data of 16 June,
2013 was geometrically rectified using control points taken
from topographic maps at 1: 250000 scale (Universal
Transverse Mercator (UTM), south 48 zone). A nearestneighbor resampling technique was used and a root mean
square error with less than 0.4 pixel was obtained.
A maximum likelihood classifier was used to classify images
into ten land cover classes: low impervious surface area,
burned peat, clear cut trees, dry soil area, primary forest,
acacia forest plantation, oil palm plantation, shrub area,
young plantation, and peat swamp forest. 183 plots were
identified during the fieldwork in the dry season of September
2014, representing 10 different land covers. Using the field
data, it was able to obtain 85 % of overall accuracy on
preliminary image classification (Figure 1b).
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
495
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Majority of the accuracy shortcoming can largely be
attributed to the heterogeneous pixel of the area, such as
burned peat, shrub, young plantation and low impervious
surface area. This has most likely contributed to errors in the
image interpretation.
3. RESEARCH METHODS
b.
3.1 High Accuracy Endmember
Endmember selection is a salient aspect in SMA analysis to
develop high-quality fraction image (Lu, 2002). Following
the procedure proposed by (Smith et al., 1985), we selected
the extreme pixel clusters which bound almost all other pixels
as endmembers using scatterplot. In this study we used two
strategies of scatterplot, global scatterplot and local
scatterplot.
The global scatterplot was used to analyse the whole area of
Pelalawan region. Hence, the entire pixels distribution in all
classes were directly analyzed and compared to an extreme
levels on the scatterplot.
The other one was local scatterplot which is the next stage to
analyze using the global scatterplot. It was used for detail
analysis of extreme pixel candidates that were detected
during global scatterplot analysis. Local scatterplot
visualization tool, available in ENVI 5.2 Classic, was used
for detail visualization. Extreme pixels in both scatterplot
were recorded and marked as endmember pixels.
To obtain the extreme values, transformation methods that
change the original data to a new dataset by reducing
redundancy, were used. However, there is a lack of
guidelines to identify which endmember selection method is
most suitable for a specific study area, hence different
authors use different methods (Lu, 2004).
This study employed Principal Component Analysis (PCA)
(Wu, 2004) and Minimum Noise Fraction (MNF) (Wu, 2003)
transformation methods.
Figure 1. (a). The study area of Pelalawan District, Riau
Province, Indonesia, (b). Supervised classification result in
Pelalawan region.
Transformed images were then extracted from the reference
image. The two transformation methods were also used to
analyze Pixel Purity Index (PPI) (Boardman, Kruse, & Green,
1995; Lu, 2002).
The PPI was used to develop pure spectral area. The pure
spectral area then used to mask the whole spectral scatterplot.
This masking results in pure spectral scatterplot which does
not contain image with mixed pixel. Hence, the final selected
endmembers were without mixed pixels.
To identify the highly accurate endmembers, best pixels
located at the extremes of the data were selected based on the
comparison between four different strategy models:
PCA/MNF spectral + global scatterplot, PCA/MNF spectral +
local scatterplot, PCA/MNF pure spectral + global scatterplot
and PCA/MNF pure spectral + local scatterplot (Figure 2).
Figure 2. The process of candidate endmember selection of each endmember combination based on four strategy models:
PCA/MNF Spectral + Global scatterplot, PCA/MNF spectral + Local Scatterplot, PCA/MNF pure spectral + Global scatterplot,
and PCA/MNF pure spectral + Local Scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
496
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
3.2 Pelalawan Fraction Model
The Pelalawan fraction model was proposed to integrate dry
land and peat land cover in Pelalawan area (Figure 3). It was
developed based on Vegetation, Impervious Surface and Soil
(V-I-S) fraction model (Ridd, 1995), and Vegetation,
Nonphotosysnthetic vegetation and shade (V-NP-S) fraction
model (Souza, 2003). Based on Pelalawan fraction model,
four kinds of endmember candidate consisted of two types of
soil (Peat Soil and Dry Soil) and two types of vegetation (Peat
Vegetation and Dry Vegetation) were used independently.
Following the procedure proposed by (Rashed et al., 2001),
endmembers is representative homogeneous pixels from
satellite images through visualizing spectral scatterplots of
image band combinations. In this study, however, the fourth
endmember, i.e., burned peat endmember was selected despite
containing heterogeneous pixels. This is because the goals of
this study is to identify burned peat distribution. Therefore the
burned peat endmember was assumed to be pure to be used as
endmember.
3.3 Spectral Mixture Analysis
Verification of the information model was done by
n-dimensional visualization tool available in ENVI based on
the Landsat PCA spectral (Figure 4). The clear delineation of
3-D visualizer of Pelalawan fraction image data
corresponding to the first three PCA components suggests that
the reflectance spectra of the OLI image might best be
represented by a four-endmember linear mixing model.
With known number of endmembers and giving the spectral
reflectance of each endmember, the observed pixel value in
any spectral band is modelled by linear combination of the
spectral response of endmember component within the pixel.
The process of solving for endmember fractions is called
spectral mixture analysis (Adams et al., 1993; Shimabukuro &
Smith,1991; Wikantika et al., 2002).
The dry vegetation endmember was identified from the areas
of shrub, peat vegetation endmember that was selected from
the areas of acacia forest, dry soil endmember was
successfully selected from dry soil, and peat soil endmember
was selected from the areas of burned peat.
Spectral mixture analysis is an application of linear algebra
with the following rules:
Rc Fi.Ri , c Ec
N
i 1
(1)
where Rc is the apparent surface reflectance in the Landsat 8
OLI band c, Fi is the fraction of endmember i, Ri,c is the
reflectance of endmember i in the Landsat 8 OLI band c. N is
the number of spectral endmembers and Ec is the error in the
Landsat 8 OLI band c to fit N endmembers.
RMSE ( ) was calculated for each pixel based on the
difference between the modelled and measured reflectance of
each pixel (Roberts et al., 1993) While the average root mean
squared (RMS) error was calculated as follows:
1 m
2
Rmea sur ed Rmod eled 2
m j 1
1
Figure 3. Pelalawan Fraction Model developed from several
model fractions that has been studied previously as a
parameter of land cover conditions in Pelalawan region
consisted of land with a high peat content (wet) and dry land.
(2)
Where m is the number of the Landsat 8 OLI bands used in
the spectral mixture analysis.
Figure 4. Three dimensional visualization model of ten land cover classes based on Pelalawan fraction model.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
497
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
4. RESULTS
4.1 Endmember Spectral Model Analysis
By conducting PCA and MNF transformations on global
scatterplot analysis, we obtained PCA and MNF components
from the OLI reflectance images used to guide image
endmember selection. The first two PCA components clearly
illustrated spatially coherent contrasts differentiating peat
soil, peat vegetation, dry soil and dry vegetation as main
component endmembers in Pelalawan fraction model. In the
case of MNF, however, transformation result failed to detect
the peat soil. Rather, the first two or three MNF components
were capable only to detect coherent contrasts differentiating
two kinds of cloud (Figure 5).
Possible reason for better performance of PCA method was
because PCA components could minimize the influence of
band to band correlation (Smith et al., 1985). On the other
hand, MNF noise variance in one band might be larger than
signal variance in another band in terms of unequal scaling in
different bands (Small, 2002). However, PPI was more
successfully transformed using MNF band composites. MNF
band component was successful to calculate repeatedly in
scatter diagram plots by iterating projection on the vector unit
which was applied to determine the purest pixels.
In order to compare the performance between fraction images
transformed by PCA scatterplot and PCA pure scatterplot
using global and local scatterplots, the RMSE for every
fraction model was calculated.
Table 1 summarizes the RMSE accuracies using different
processing models. The pixel value below 0 or more than 1 in
all endmember fraction were classified as outlier. Because of
this, almost 40% of the data in PCA pure spectral value were
lost. This is why the accuracy value of PCA pure spectral value
was relatively small compared with the models that did not use
the PPI masking. Therefore, the use of PPI masking is not
recommended, especially in monitoring burned peat class.
RMSE of the fraction image transformed by PCA spectral
using local scatterplot was 0.232156, where the value is
slightly smaller than the 0.232342 of PCA scatterplot using
global scatterplot which suggests both of strategies were
generally good fit (less than 0.5). This implies that fraction
images with PCA spectral using local scatterplot method
without PPI analysis can be the best combination strategy to
detect endmember in Pelalawan region.
Table 1. Statistic assessment comparisons of four endmembers
fraction image and estimation accuracy for four endmember
selection strategies.
Based on the above results, endmember fractions in PCA
spectral global scatterplot, PCA spectral local scatterplot,
PCA pure global scatterplot, and PCA pure spectral local
scatterplot combinations were calculated by solving a fully
constrained four-endmember linear mixing model using the
Landsat 8 OLI reflectance data. Figure 6 shows comparisons
between reflectance image, PPI image and PCA image to
create PCA pure spectral using local scatterplot.
Figure 5. Comparison between PCA transformations and the first-three components result (right) and MNF transformations
and the first-three components result (left) on global scatterplot analysis endmember reflectance spectra identified through
the analysis of the scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
498
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Figure 6. Local scatterplot representation of the first-two PCA components combined with PPI analysis and four endmember
reflectance spectra identified through the analysis of the scatterplot.
4.2 Burned Peat Endmember Fraction Image Analysis
Theoretically, if all of the pixels are within a quadrangle that
is formed by endmembers, a mixture model can be
considered as an ideal linear model. In this study, there is an
outward curvature along the edge of the peat vegetation and
dry vegetation and inward curvature along the edge of dry
soil and dry vegetation in the scatter plot of PCA components
1 and 2 (Figure 7). This implies that existence of some
nonlinear mixture among these endmembers. Moreover, the
selection of peat soil endmember (burned peat class) is
difficult because it does not cluster well in the scatterplot. We
selected this endmember from highly reflected burned peat in
the Pelalawan area because of the study’s special interest on
degraded peatland especially burned peat surface.
One consequence was fraction values of pixels lying outside
could not achieve the range of 0 to 1, and it did not meet the
Adams et al., (1993) criteria. He proposed to inspect the
histograms of fraction images and quantify percentage of
pixels lying outside the range of 0 to 1and to evaluate fraction
value consistency over time. Only model with at least 98%
of the values within 0 to 1 and those that showed mean
fraction value consistent over time were kept.
Figure 7. Scatterplot of endmember spectra in based on
Pelalawan Fraction Line.
Overall, the endmember spectra shows that this model
represents shrub , acacia forest, dry soil, and burned peat cover
types very well because among whole endmember fractions,
pixels fraction distribution remained predominantly in the
range of 0 to 1. However, the performance was not
satisfactory for modelling some endmember fraction material
in the study area.
Figure 8 shows the distribution of burned peat fraction based
on PCA pure spectral transformation using local scatterplot.
Although the fraction statistic value results showed that the
burned peat fraction range distributed from -4.16 to 10.73,
most of the fraction values were distributed in the range of
0-1 (figure 9). Acacia class and some parts of peat swamp
forest class had a fraction value less than 0. Therefore, the
faction of burned peat assumed these classes as outlier with
no value in the fraction image.
Figure 9. Histogram of burned peat fraction derived from
PCA spectral using local scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
499
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Figure 8. Burned peat fraction imagery derived from PCA spectral using local scatterplot.
The analysis of the ground verification showed that the burned
peat area can be accurately detected only above 40%.
Therefore, the calculation of the area of burned peat in
Pelalawan region was calculated from the value percentage of
40% and above (Figure 8).
The calculation of total burned Peat area above 40% using
SMA resulted in 428.93 km2 which was less than the area of
898.50 km2 calculated using maximum likelihood. One reason
for the disparity is that, unlike the maximum likelihood, the
SMA faction image can distinguish burned peat and peat
water. Another reason is SMA can better discriminate
between different classes in mixed pixel class than the
maximum likelihood approach.
5. CONCLUSION
This study evaluated two different image transformations:
i.e., PCA, MNF and two different scatterplot, i.e. global and
local, to detect the best endmember which is vital to obtain
high quality burned peat fraction image. Analysis of image
dimensionality was reduced by using PCA. Pixel Purity
Index (PPI), formed by using MNF, was used to identify pure
pixel. The result showed that PCA spectral without PPI
masking using local scatterplot analysis can successfully
detect high accuracy endmember and reduces RMSE value
of fraction image to improve classification accuracy.
Hence, fraction image with the scatterplot method could
achieve the best combination strategy. However, the burned
peat fraction image performance was not satisfactory for
modelling all fractions of the study area such as acacia forest
and peat swamp because of the linear assumption used in the
analysis. Therefore, future research should target testing nonlinear Spectral Mixture Analysis application, using multi
temporal data imagery, improving accuracy assessment
technic and comparing it with results from current research.
6. ACKNOWLEDGMENTS
The authors are grateful to acknowledge the support from
Global Forest Environmental Studies Laboratory, The
University of Tokyo and also supported through the Global
Leader Program for Social Design and Management (GSDM)
by the Ministry of Education, Culture, Sport, Science and
Technology Japan.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
500
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
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This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
501
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
SPECTRAL MIXTURE ANALYSIS (SMA) OF LANDSAT IMAGERY FOR LAND
COVER STUDY OF HIGHLY DEGRADED PEATLAND IN INDONESIA
A. D. Saktia,b*, S. Tsuyuki b
a.Center
b. Global
for Remote Sensing, Bandung Institute of Technology, Bandung, Indonesia -anjardimara@yahoo.com
Forest Environmental Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
- tsuyuki@fr.a.u-tokyo.ac.jp
Commission VI, WG VI/4
KEY WORDS: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction,
Pelalawan District.
ABSTRACT:
Indonesian peatland, one of the world’s largest tropical peatlands, is facing immense anthropogenic pressures such as illegal
logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to
assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of
land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum
Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were
compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was
used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation
(peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were
obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect
high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.
1. INTRODUCTION
Indonesia is one of the countries having the largest tropical
peatlands area in the world. The country’s peatland is facing
immense anthropogenic pressures such as illegal logging,
degradation and peat fires, especially in fertile peatlands
(Margono, 2014). However, there still is a knowledge gap in
terms of understanding extent of pressure faced by peatlands
as well as appropriate tools to assess land cover change.
Remote sensing technology is one of the useful tools for
monitoring condition of forest ecosystem such as
deforestation and forest degradation (Souza, 2005).
Land-cover classification using medium spatial resolution
data such as Landsat is often difficult because of limitations
in spatial resolution of the data and the heterogeneity of
features on the ground especially for successional stages in a
degraded tropical forest region (Souza, 2000; Stone, 1998;
Eraldo, 2010). This problem often produces poor
classification accuracy when conventional algorithms such as
the maximum likelihood classifier are used. Several methods
have been developed to solve the mixed pixels problem, one
of them being the Spectral Mixture Analysis (SMA).
The main objective of this paper is to determine number of
land cover endmembers that can be detected and mapped
using new generation of Landsat 8 OLI in order to develop
high-quality burned peat fraction images.
* Corresponding author
In order to achieve the objective, two different image
transformations, pure pixel identification method, and two
different scatterplot strategies were tested and their accuracy
results were compared.
2. STUDY AREA AND PRE-ANALYSIS OF
IMAGERY DATA
The study was conducted in the Pelalawan district, one of the
highly degraded peatland area in Indonesia (Tanuwijaya et
al., 2014; Supriatna, 2014). It is located between 100˚42' and
103˚28' E longitudes and 1˚25' and 0˚20' S latitudes, and the
district is 77 km from Pekanbaru (the capital of Riau
province) (Figure 1a). Land cover condition in Pelalawan
region consisted of land with a high peat content (wet) and
dry land (Meilani et al., 2011). Landsat 8 OLI data of 16 June,
2013 was geometrically rectified using control points taken
from topographic maps at 1: 250000 scale (Universal
Transverse Mercator (UTM), south 48 zone). A nearestneighbor resampling technique was used and a root mean
square error with less than 0.4 pixel was obtained.
A maximum likelihood classifier was used to classify images
into ten land cover classes: low impervious surface area,
burned peat, clear cut trees, dry soil area, primary forest,
acacia forest plantation, oil palm plantation, shrub area,
young plantation, and peat swamp forest. 183 plots were
identified during the fieldwork in the dry season of September
2014, representing 10 different land covers. Using the field
data, it was able to obtain 85 % of overall accuracy on
preliminary image classification (Figure 1b).
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Majority of the accuracy shortcoming can largely be
attributed to the heterogeneous pixel of the area, such as
burned peat, shrub, young plantation and low impervious
surface area. This has most likely contributed to errors in the
image interpretation.
3. RESEARCH METHODS
b.
3.1 High Accuracy Endmember
Endmember selection is a salient aspect in SMA analysis to
develop high-quality fraction image (Lu, 2002). Following
the procedure proposed by (Smith et al., 1985), we selected
the extreme pixel clusters which bound almost all other pixels
as endmembers using scatterplot. In this study we used two
strategies of scatterplot, global scatterplot and local
scatterplot.
The global scatterplot was used to analyse the whole area of
Pelalawan region. Hence, the entire pixels distribution in all
classes were directly analyzed and compared to an extreme
levels on the scatterplot.
The other one was local scatterplot which is the next stage to
analyze using the global scatterplot. It was used for detail
analysis of extreme pixel candidates that were detected
during global scatterplot analysis. Local scatterplot
visualization tool, available in ENVI 5.2 Classic, was used
for detail visualization. Extreme pixels in both scatterplot
were recorded and marked as endmember pixels.
To obtain the extreme values, transformation methods that
change the original data to a new dataset by reducing
redundancy, were used. However, there is a lack of
guidelines to identify which endmember selection method is
most suitable for a specific study area, hence different
authors use different methods (Lu, 2004).
This study employed Principal Component Analysis (PCA)
(Wu, 2004) and Minimum Noise Fraction (MNF) (Wu, 2003)
transformation methods.
Figure 1. (a). The study area of Pelalawan District, Riau
Province, Indonesia, (b). Supervised classification result in
Pelalawan region.
Transformed images were then extracted from the reference
image. The two transformation methods were also used to
analyze Pixel Purity Index (PPI) (Boardman, Kruse, & Green,
1995; Lu, 2002).
The PPI was used to develop pure spectral area. The pure
spectral area then used to mask the whole spectral scatterplot.
This masking results in pure spectral scatterplot which does
not contain image with mixed pixel. Hence, the final selected
endmembers were without mixed pixels.
To identify the highly accurate endmembers, best pixels
located at the extremes of the data were selected based on the
comparison between four different strategy models:
PCA/MNF spectral + global scatterplot, PCA/MNF spectral +
local scatterplot, PCA/MNF pure spectral + global scatterplot
and PCA/MNF pure spectral + local scatterplot (Figure 2).
Figure 2. The process of candidate endmember selection of each endmember combination based on four strategy models:
PCA/MNF Spectral + Global scatterplot, PCA/MNF spectral + Local Scatterplot, PCA/MNF pure spectral + Global scatterplot,
and PCA/MNF pure spectral + Local Scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
3.2 Pelalawan Fraction Model
The Pelalawan fraction model was proposed to integrate dry
land and peat land cover in Pelalawan area (Figure 3). It was
developed based on Vegetation, Impervious Surface and Soil
(V-I-S) fraction model (Ridd, 1995), and Vegetation,
Nonphotosysnthetic vegetation and shade (V-NP-S) fraction
model (Souza, 2003). Based on Pelalawan fraction model,
four kinds of endmember candidate consisted of two types of
soil (Peat Soil and Dry Soil) and two types of vegetation (Peat
Vegetation and Dry Vegetation) were used independently.
Following the procedure proposed by (Rashed et al., 2001),
endmembers is representative homogeneous pixels from
satellite images through visualizing spectral scatterplots of
image band combinations. In this study, however, the fourth
endmember, i.e., burned peat endmember was selected despite
containing heterogeneous pixels. This is because the goals of
this study is to identify burned peat distribution. Therefore the
burned peat endmember was assumed to be pure to be used as
endmember.
3.3 Spectral Mixture Analysis
Verification of the information model was done by
n-dimensional visualization tool available in ENVI based on
the Landsat PCA spectral (Figure 4). The clear delineation of
3-D visualizer of Pelalawan fraction image data
corresponding to the first three PCA components suggests that
the reflectance spectra of the OLI image might best be
represented by a four-endmember linear mixing model.
With known number of endmembers and giving the spectral
reflectance of each endmember, the observed pixel value in
any spectral band is modelled by linear combination of the
spectral response of endmember component within the pixel.
The process of solving for endmember fractions is called
spectral mixture analysis (Adams et al., 1993; Shimabukuro &
Smith,1991; Wikantika et al., 2002).
The dry vegetation endmember was identified from the areas
of shrub, peat vegetation endmember that was selected from
the areas of acacia forest, dry soil endmember was
successfully selected from dry soil, and peat soil endmember
was selected from the areas of burned peat.
Spectral mixture analysis is an application of linear algebra
with the following rules:
Rc Fi.Ri , c Ec
N
i 1
(1)
where Rc is the apparent surface reflectance in the Landsat 8
OLI band c, Fi is the fraction of endmember i, Ri,c is the
reflectance of endmember i in the Landsat 8 OLI band c. N is
the number of spectral endmembers and Ec is the error in the
Landsat 8 OLI band c to fit N endmembers.
RMSE ( ) was calculated for each pixel based on the
difference between the modelled and measured reflectance of
each pixel (Roberts et al., 1993) While the average root mean
squared (RMS) error was calculated as follows:
1 m
2
Rmea sur ed Rmod eled 2
m j 1
1
Figure 3. Pelalawan Fraction Model developed from several
model fractions that has been studied previously as a
parameter of land cover conditions in Pelalawan region
consisted of land with a high peat content (wet) and dry land.
(2)
Where m is the number of the Landsat 8 OLI bands used in
the spectral mixture analysis.
Figure 4. Three dimensional visualization model of ten land cover classes based on Pelalawan fraction model.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
4. RESULTS
4.1 Endmember Spectral Model Analysis
By conducting PCA and MNF transformations on global
scatterplot analysis, we obtained PCA and MNF components
from the OLI reflectance images used to guide image
endmember selection. The first two PCA components clearly
illustrated spatially coherent contrasts differentiating peat
soil, peat vegetation, dry soil and dry vegetation as main
component endmembers in Pelalawan fraction model. In the
case of MNF, however, transformation result failed to detect
the peat soil. Rather, the first two or three MNF components
were capable only to detect coherent contrasts differentiating
two kinds of cloud (Figure 5).
Possible reason for better performance of PCA method was
because PCA components could minimize the influence of
band to band correlation (Smith et al., 1985). On the other
hand, MNF noise variance in one band might be larger than
signal variance in another band in terms of unequal scaling in
different bands (Small, 2002). However, PPI was more
successfully transformed using MNF band composites. MNF
band component was successful to calculate repeatedly in
scatter diagram plots by iterating projection on the vector unit
which was applied to determine the purest pixels.
In order to compare the performance between fraction images
transformed by PCA scatterplot and PCA pure scatterplot
using global and local scatterplots, the RMSE for every
fraction model was calculated.
Table 1 summarizes the RMSE accuracies using different
processing models. The pixel value below 0 or more than 1 in
all endmember fraction were classified as outlier. Because of
this, almost 40% of the data in PCA pure spectral value were
lost. This is why the accuracy value of PCA pure spectral value
was relatively small compared with the models that did not use
the PPI masking. Therefore, the use of PPI masking is not
recommended, especially in monitoring burned peat class.
RMSE of the fraction image transformed by PCA spectral
using local scatterplot was 0.232156, where the value is
slightly smaller than the 0.232342 of PCA scatterplot using
global scatterplot which suggests both of strategies were
generally good fit (less than 0.5). This implies that fraction
images with PCA spectral using local scatterplot method
without PPI analysis can be the best combination strategy to
detect endmember in Pelalawan region.
Table 1. Statistic assessment comparisons of four endmembers
fraction image and estimation accuracy for four endmember
selection strategies.
Based on the above results, endmember fractions in PCA
spectral global scatterplot, PCA spectral local scatterplot,
PCA pure global scatterplot, and PCA pure spectral local
scatterplot combinations were calculated by solving a fully
constrained four-endmember linear mixing model using the
Landsat 8 OLI reflectance data. Figure 6 shows comparisons
between reflectance image, PPI image and PCA image to
create PCA pure spectral using local scatterplot.
Figure 5. Comparison between PCA transformations and the first-three components result (right) and MNF transformations
and the first-three components result (left) on global scatterplot analysis endmember reflectance spectra identified through
the analysis of the scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
498
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Figure 6. Local scatterplot representation of the first-two PCA components combined with PPI analysis and four endmember
reflectance spectra identified through the analysis of the scatterplot.
4.2 Burned Peat Endmember Fraction Image Analysis
Theoretically, if all of the pixels are within a quadrangle that
is formed by endmembers, a mixture model can be
considered as an ideal linear model. In this study, there is an
outward curvature along the edge of the peat vegetation and
dry vegetation and inward curvature along the edge of dry
soil and dry vegetation in the scatter plot of PCA components
1 and 2 (Figure 7). This implies that existence of some
nonlinear mixture among these endmembers. Moreover, the
selection of peat soil endmember (burned peat class) is
difficult because it does not cluster well in the scatterplot. We
selected this endmember from highly reflected burned peat in
the Pelalawan area because of the study’s special interest on
degraded peatland especially burned peat surface.
One consequence was fraction values of pixels lying outside
could not achieve the range of 0 to 1, and it did not meet the
Adams et al., (1993) criteria. He proposed to inspect the
histograms of fraction images and quantify percentage of
pixels lying outside the range of 0 to 1and to evaluate fraction
value consistency over time. Only model with at least 98%
of the values within 0 to 1 and those that showed mean
fraction value consistent over time were kept.
Figure 7. Scatterplot of endmember spectra in based on
Pelalawan Fraction Line.
Overall, the endmember spectra shows that this model
represents shrub , acacia forest, dry soil, and burned peat cover
types very well because among whole endmember fractions,
pixels fraction distribution remained predominantly in the
range of 0 to 1. However, the performance was not
satisfactory for modelling some endmember fraction material
in the study area.
Figure 8 shows the distribution of burned peat fraction based
on PCA pure spectral transformation using local scatterplot.
Although the fraction statistic value results showed that the
burned peat fraction range distributed from -4.16 to 10.73,
most of the fraction values were distributed in the range of
0-1 (figure 9). Acacia class and some parts of peat swamp
forest class had a fraction value less than 0. Therefore, the
faction of burned peat assumed these classes as outlier with
no value in the fraction image.
Figure 9. Histogram of burned peat fraction derived from
PCA spectral using local scatterplot.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
499
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
Figure 8. Burned peat fraction imagery derived from PCA spectral using local scatterplot.
The analysis of the ground verification showed that the burned
peat area can be accurately detected only above 40%.
Therefore, the calculation of the area of burned peat in
Pelalawan region was calculated from the value percentage of
40% and above (Figure 8).
The calculation of total burned Peat area above 40% using
SMA resulted in 428.93 km2 which was less than the area of
898.50 km2 calculated using maximum likelihood. One reason
for the disparity is that, unlike the maximum likelihood, the
SMA faction image can distinguish burned peat and peat
water. Another reason is SMA can better discriminate
between different classes in mixed pixel class than the
maximum likelihood approach.
5. CONCLUSION
This study evaluated two different image transformations:
i.e., PCA, MNF and two different scatterplot, i.e. global and
local, to detect the best endmember which is vital to obtain
high quality burned peat fraction image. Analysis of image
dimensionality was reduced by using PCA. Pixel Purity
Index (PPI), formed by using MNF, was used to identify pure
pixel. The result showed that PCA spectral without PPI
masking using local scatterplot analysis can successfully
detect high accuracy endmember and reduces RMSE value
of fraction image to improve classification accuracy.
Hence, fraction image with the scatterplot method could
achieve the best combination strategy. However, the burned
peat fraction image performance was not satisfactory for
modelling all fractions of the study area such as acacia forest
and peat swamp because of the linear assumption used in the
analysis. Therefore, future research should target testing nonlinear Spectral Mixture Analysis application, using multi
temporal data imagery, improving accuracy assessment
technic and comparing it with results from current research.
6. ACKNOWLEDGMENTS
The authors are grateful to acknowledge the support from
Global Forest Environmental Studies Laboratory, The
University of Tokyo and also supported through the Global
Leader Program for Social Design and Management (GSDM)
by the Ministry of Education, Culture, Sport, Science and
Technology Japan.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-W3-495-2015
500
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015
36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
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